Artificial intelligence (AI) is driving business transformation across industries. In the manufacturing and distribution industry, experts predict that the increasing implementation of AI will be more modernistic than the introduction of automation and mass production in the 1950s. By 2028, the value of AI in the global manufacturing market is projected to reach $20.8 billion, up from $3.2 billion in 2023.

While AI burst into the mainstream in 2023, it’s been incrementally transforming manufacturing operations for decades. Today, several types of AI can be applied to help manufacturing and distribution firms streamline processes, improve product quality and lower costs.

• Machine learning enables systems to learn from data, identify patterns and make decisions. Manufacturers apply machine learning to predict demand, optimize processes and improve quality control.
• Robotic process automation uses software bots to automate repetitive tasks, freeing up skilled workers to concentrate on other tasks.
• Natural language processing helps systems understand, interpret and understand language. It’s most often used to support and automate customer service, but it can also assist in translating technical training manuals.
• Expert systems are AI platforms that leverage analytical capabilities to advise the user. Manufacturers can use the expert systems to troubleshoot operations, maintain equipment and design products. 
• Computer vision enables machines to interpret the world visually. Manufacturing firms leverage computer vision to enhance quality control, process automation and safety. AI-powered computer vision is able to detect product defects with greater accuracy than humans, helping to improve product quality and consistency.
• Generative AI creates new content based on learning patterns and data structures. For manufacturers, generative AI has applications across product design and production processes.

3 Emerging AI Opportunities

As AI continues to evolve rapidly, so do the operational opportunities for manufacturing and distribution firms. Three key trends are poised to have a significant impact on the industry in the short term.

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1) Edge analytics empower real-time analytics and decision-making.

The increased use of operational technology (OT) and connected devices throughout manufacturing processes is driving the adoption of edge analytics. With edge analytics, AI enables manufacturers to analyze data closer to its source instead of transferring it to a cloud-based centralized data center. By conducting analysis at the “edge of the network,” manufacturing firms gain real-time insights from data provided by OT sensors, machinery and equipment.

Edge analytics is a game changer for predictive maintenance. Manufacturers can analyze data inputs from equipment to predict anomalies or failures in real time and take action to schedule proactive maintenance and interventions. These actions can significantly reduce downtime, optimize equipment use and increase operational efficiencies.

2) Integrated AI and automation systems fuel increased operational agility.

Manufacturing and distribution firms have new opportunities to increase the flexibility of automation systems traditionally designed for specific tasks or products. The integration of AI makes it easier to adapt to new designs, scheduling demands and process variations. AI can analyze and learn in real time, enabling the automation systems to be reconfigured quicker, reducing retooling costs and operational downtime.

Similarly, AI’s analysis capabilities across a range of data sources, such as orders, inventory and production, allow manufacturing and distribution firms to dynamically adjust production, scheduling and resource allocation in response to shifts in demand. This added flexibility is especially beneficial for firms that produce customized products, require small batch runs or frequently change specifications.

3) Generative AI is reshaping the industry.

Unlike AI models that analyze data and provide decision-support, generative AI systems are trained over time to produce original content in various forms, including text, code, images and video. In the manufacturing and distribution industry, generative AI can be applied to enhance design, operations, supply-chain management and customer service processes.

Generative AI can help engineers accelerate the design process by generating concepts based on specific requirements. It can also generate new product ideas. When used as a tool throughout the design process, AI can enable faster iteration, reduce time to market and inspire innovation.

Related to operations, generative AI can analyze, streamline and adapt logistics, including route plans, load plans and scheduling. At the same time, it can monitor supply chains and supplier performance, alerting firms to potential disruptions and suggesting mitigation strategies.

Balancing AI Advantages and Risks

As manufacturers continue to implement AI, they must balance the immense opportunities with the risks that come with the relatively young and rapidly developing technology. AI systems can be expensive, requiring firms to invest in hardware, software and specialized expertise. In addition, the increasing connectivity of equipment, vendor integration and use of real-time analytics can put confidential data at risk. Manufacturers must enhance cybersecurity strategies to protect against unauthorized network access, data breaches, hacking and ransom attacks.

AI guidelines, governance and best practices are evolving alongside the technology, requiring manufacturers to monitor and assess risks associated with AI across the organization’s official use as well as all contractor, vendor and employee use. Firms should consider AI-related risks, transparency requirements and governance measures as they expand AI use and implement new tools.

The manufacturing and distribution industry experts at CBIZ can help you navigate AI opportunities, operational implications and potential risks. Connect with a member of our team and gain access to more resources here. 

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